scholarly journals Prediction with missing data via Bayesian Additive Regression Trees

2015 ◽  
Vol 43 (2) ◽  
pp. 224-239 ◽  
Author(s):  
Adam Kapelner ◽  
Justin Bleich
Keyword(s):  
Missing Data ◽  
Regression Trees ◽  
Additive Regression ◽  
Bayesian Additive Regression Trees

scholarly journals BART: Bayesian additive regression trees

2010 ◽  
Vol 4 (1) ◽  
pp. 266-298 ◽  
Author(s):  
Hugh A. Chipman ◽  
Edward I. George ◽  
Robert E. McCulloch
Keyword(s):  
Regression Trees ◽  
Additive Regression ◽  
Bayesian Additive Regression Trees

Variable Selection and Interaction Detection with Bayesian Additive Regression Trees

2021 ◽  
pp. 395-414
Author(s):  
Carlos M. Carvalho ◽  
Edward I. George ◽  
P. Richard Hahn ◽  
Robert E. McCulloch
Keyword(s):  
Variable Selection ◽  
Regression Trees ◽  
Interaction Detection ◽  
Additive Regression ◽  
Bayesian Additive Regression Trees

scholarly journals Estimation of causal effects of multiple treatments in observational studies with a binary outcome

2020 ◽  
Vol 29 (11) ◽  
pp. 3218-3234 ◽  
Author(s):  
Liangyuan Hu ◽  
Chenyang Gu ◽  
Michael Lopez ◽  
Jiayi Ji ◽  
Juan Wisnivesky
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Regression Trees ◽  
Likelihood Estimator ◽  
Inverse Probability ◽  
Common Support ◽  
Targeted Maximum Likelihood ◽  
Additive Regression ◽  
Multiple Treatments ◽  
Bayesian Additive Regression Trees

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.

scholarly journals Bayesian additive regression trees and the General BART model

2019 ◽  
Vol 38 (25) ◽  
pp. 5048-5069 ◽  
Author(s):  
Yaoyuan Vincent Tan ◽  
Jason Roy
Keyword(s):  
Regression Trees ◽  
Additive Regression ◽  
Bayesian Additive Regression Trees
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